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IMPROVING THE QUALITY OF RFID DATA BY UTILISING A BAYESIAN NETWORK CLEANING METHOD

机译:利用贝叶斯网络清洗方法提高RFID数据的质量

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Radio Frequency Identification (RFID) is a technology used to identify automatically a cluster of objects within a specified parameter. This technology has promised a means to cut cost of time and money in manual labor and to allow greater efficiency in numerous workplaces. However, there are various problems such as missed readings which hinder wide scale adoption of RFID systems. To this end we propose a system that utilises a Bayesian Network applied at a Deferred stage to impute and restore missed readings. Experimental results have shown that the optimal random threshold is 15% and that the DefBayNet method improves missed data restoration process when compared with the state-of-the-art method.
机译:射频识别(RFID)是一种用于自动识别指定参数内的对象簇的技术。这项技术有望减少人工劳动的时间和金钱,并在许多工作场所提高效率。但是,存在各种问题,例如读数遗漏,阻碍了RFID系统的大规模采用。为此,我们提出了一种系统,该系统利用在递延阶段应用的贝叶斯网络来估算和恢复丢失的读数。实验结果表明,最佳随机阈值为15%,并且与最新方法相比,DefBayNet方法可改善丢失的数据恢复过程。

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